This is being hyped as "mind-blowing," supposedly "describing how the future of AI might look like." I suspect these claims are somewhat overblown, but I haven't had a chance to read the paper yet.

This is the abstract:

For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, per- mitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural net- work algorithm that uses agents embedded in the neural net- work whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tour- nament selection genetic algorithm is used to select path- ways through the neural network for replication and muta- tion. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classifica- tion tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicabil- ity for neural network training. Finally, PathNet also signif- icantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).

Medium published an article on it, and you can see a video of PathNet in action here.